Google Brain Creates Technology that Can Zoom In and Enhance Pixelated Images

As Ars Techinca breaks down, the software utilizes a two different neural networks to generate the images. Easier done in fiction, of course, because a basic principle in computer graphics is that you can't extract more data, that is, a higher resolution image, out of a very low res image.

Input: left column. Fourth column: the original image.

The way Google Brain handles this task is it first compares the 64-pixel source with other higher resolution images which were downsized to the same 8×8 grid.

The team at Google Brain has made an impressive breakthrough for increasing the resolution of images. The middle column shows what Google was able to derive from the pixelated images.

The finished product from the prior network and the conditioning network is combined into one to produce variations.

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Once the first AI network has completed its role, the Google researchers use the PixelCNN to add extra pixels to the 8x8 image. The network then adds new pixels one by one in a way that matches what the machine already knows.

To create the final super-resolution image, the outputs from the two neural networks are mashed together. It then created a probability distribution for each lower-resolution pixel, essentially building a map of likelihoods for what each chunky pixel might contain. ArsTechnica references an example of a brown pixel being identified as an eyebrow, which Google Brain fills in with eyebrow-like pixels. Usually, this results in a plausible and probable result.

There's obviously a ways to go before this implementation could push for mainstream use, but now that we know image-enhancing is indeed a possibility, crime shows can at least have something to fall back on when accused of the trope.

More impressively, Google Brain's technique was successful in real-word testing. Human observers were shown the resulting upscaled computed image. When human observers were shown a real high-resolution celebrity face vs. the upscaled computed image, they were fooled 10 percent of the time (50 percent would be a flawless score).

One of the best videos of all time. I wonder too, if this sort of algorithmic method will make it possible to upscale a lot of old images in the future, to something resembling high-resolution. The added details-known as "hallucinations" in image processing jargon-are a best guess and nothing more. The results might not be flawless as it is in Hollywood, but it could help to narrow down the list of suspects.

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